The exact training run is basically impossible anyway. Randomness plays a role. Even if you fix your RNG seed, in a distributed training scenario like this one some weight updates might come at different times and be included in different update steps. Should have minimal impact on the final outcome, but would still be a different model as some of the weights will differ in the end.
Reproducing a stationary probability distribution that subsequent runs draw from is also a kind of reproduction. And presumably for ffmpeg you can fix the random seed? Tell me more about that design, please.
